Adaptive Control of Electric Drives Using Sliding-Mode Learning Neural Networks G. L. Cascella * , F. Cupertino * , A. V. Topalov ** † , O. Kaynak ** and V. Giordano * * Politecnico di Bari/Dipartimento di Elettrotecnica ed Elettronica, Via Re David, 200-70125 Bari, Italy ** Bogazici University/Electrical and Electronic Engineering Department, Bebek, 34342 Istanbul, Turkey † On leave from Technical University Sofia – Plovdiv branch/Control Systems Department, 4000 Plovdiv, Bulgaria Abstract—New sliding mode control theory-based method for on-line learning in multilayer neural controllers as applied to the speed control of electric drives is presented. The proposed algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives. I. II. INTRODUCTION In those applications where the knowledge of the system to be controlled is fragmentary or obtainable only in a costly way through complex off-line experiments, artificial neural networks (NNs) can be an effective instrument to learn from input-output data and efficiently catch information about the most appropriate control action to apply. However the application of NNs in feedback control systems requires the study of their properties such as stability and robustness to environmental disturbances and structural uncertainties before drawing conclusions about the performances of the overall system [1]. Moreover, in neuro-adaptive systems, in order to compensate for the existing variable and unpredictable disturbances and changes in the plant parameters, robust and fast on-line learning of the neural controller is a key issue. Recently, Variable Structure Systems (VSS)-based algorithms have been proposed for on-line tuning of NNs, showing very interesting properties and proving to be faster and more robust than the traditional learning techniques. One of the first studies on adaptive learning in single layer network architectures is due to Ramirez et al. [2]. In another paper [3], the existence of a relation between sliding surface for the plant to be controlled and the zero learning error level of the parameters of the single layer neuro-controller is discussed and the control applications of the method considered in [2] are studied. Differently from [2, 3], the sliding mode algorithms proposed in [4, 5] are for training of multilayer NNs which do not have the limited approximation capabilities of the single layer networks. Although the results presented in [5] are quite encouraging, they have been obtained through simulation analysis only. The main goal of this work is to prove experimentally the effectiveness of the above algorithm for training of MFNN-based controllers in non-linear feedback control systems. It is also shown that the results obtained in [3] can be also extended to the MFNN controllers. The control applications studied are the speed control of a permanent magnet synchronous motor (PMSM) and of an induction motor (IM) with a nonlinear centrifugal load provided by a fan. In industrial applications, PMSM and IM drives are widely used, due to their inherent features such as versatility, ruggedness and precision. However in some applications, when uncertainties and disturbances are appreciable, traditional control techniques are not able to guarantee optimal performances or can require a considerably time- consuming and plant-dependent design stage. This has recently motivated a considerable amount of research in the field of NNs-based control of electric drives, in order to exploit the property of NNs to learn complex nonlinear mappings [6-10]. In industrial settings, the most widely used controller is still the Proportional-Integral-Derivative (PID) one and the spread of neural controllers for electric drives is contingent on the satisfaction of some critical requirements. Apart from guaranteeing good performance in a wide range of operating conditions, the computational burden presented by the neural controller should be low enough to allow its implementation on low-cost microcontrollers. Furthermore, even in the presence of a fragmentary knowledge of the plant parameters, the start- up procedure (choice of the learning rate, number of the neurons and the network layers, inputs and outputs, as well as the desired NN output) should be fast, straightforward and as general as possible, i.e. applicable for different motors and drives and thus reducing the necessary installation time, with remarkable and captivating cost savings. The main body of the paper contains five sections. Section II gives the definitions and the formulation of the problem. Section III introduces the equivalency constraints on the sliding control performance for the plant and sliding mode learning performance for the controller. Section IV presents the experimental application of the proposed control scheme to the electrical drives. Finally, section V summarizes the results of this investigation and discusses further improvements. BASIC ASSUMPTIONS AND PROBLEM FORMULATION Consider a three layer MFNN that is to be used as a neural controller. The following definitions will be used: IEEE ISIE 2005, June 20-23, 2005, Dubrovnik, Croatia 0-7803-8738-4/05/$20.00 ©2005 IEEE 125